Nanobody in a Double "Y"-Shaped Assembly: A Promising Candidate for Lateral Flow Immunoassays.

Derived from camelid heavy-chain antibodies, nanobodies (Nbs) are the smallest natural antibodies and are an ideal tool in biological studies because of their simple structure, high yield, and low cost. Nbs possess significant potential for developing highly specific and user-friendly diagnostic assays. Despite offering considerable advantages in detection applications, knowledge is limited regarding the exclusive use of Nbs in lateral flow immunoassay (LFIA) detection. Herein, we present a novel double "Y" architecture, achieved by using the SpyTag/SpyCatcher and Im7/CL7 systems. The double "Y" assemblies exhibited a significantly higher affinity for their epitopes, as particularly evident in the reduced dissociation rate. An LFIA employing double "Y" assemblies was effectively used to detect the severe acute respiratory syndrome coronavirus-2 N protein, with a detection limit of at least 500 pg/mL. This study helps broaden the array of tools available for the development of Nb-based diagnostic techniques.

Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing.

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs.

LncRNA-protein interaction prediction with reweighted feature selection.

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

Targeting GDP-Dissociation Inhibitor Beta (GDI2) with a Benzo[a]quinolizidine Library to Induce Paraptosis for Cancer Therapy.

Inducing paraptosis, a nonapoptotic form of cell death, has great therapeutic potential in cancer therapy, especially for drug-resistant tumors. However, the specific molecular target(s) that trigger paraptosis have not yet been deciphered yet. Herein, by using activity-based protein profiling, we identified the GDP-dissociation inhibitor beta (GDI2) as a manipulable target for inducing paraptosis and uncovered benzo[a]quinolizidine BQZ-485 as a potent inhibitor of GDI2 through the interaction with Tyr245. Comprehensive target validation revealed that BQZ-485 disrupts the intrinsic GDI2-Rab1A interaction, thereby abolishing vesicular transport from the endoplasmic reticulum (ER) to the Golgi apparatus and initiating subsequent paraptosis events including ER dilation and fusion, ER stress, the unfolded protein response, and cytoplasmic vacuolization. Based on the structure of BQZ-485, we created a small benzo[a]quinolizidine library by click chemistry and discovered more potent GDI2 inhibitors using a NanoLuc-based screening platform. Leveraging the engagement of BQZ-485 with GDI2, we developed a selective GDI2 degrader. The optimized inhibitor (+)-37 and degrader 21 described in this study exhibited excellent in vivo antitumor activity in two GDI2-overexpressing pancreatic xenograft models, including an AsPc-1 solid tumor model and a transplanted human PDAC tumor model. Altogether, our findings provide a promising strategy for targeting GDI2 for paraptosis in the treatment of pancreatic cancers, and these lead compounds could be further optimized to be effective chemotherapeutics.

RT-IVT method allows multiplex real-time quantification of in vitro transcriptional mRNA production.

For the past 30 years, in vitro transcription (IVT) technology has been extensively used for RNA production or for basic transcriptional mechanism research. However, methods for mRNA quantification still need to be improved. In this study, we designed a RT-IVT method using binary fluorescence quencher (BFQ) probes and the PBCV-1 DNA ligase to quantify mRNA production in real-time by fluorescence resonance energy transfer (FRET) and RNA-splinted DNA ligation. Compared with existing methods, the RT-IVT method is inexpensive and non-radioactive, and can detect mRNA production in unpurified systems in real-time and shows high sensitivity and selectivity. The activity of T7 RNA polymerase and Escherichia coli RNA polymerase holoenzyme was then characterized with this method. We then multiplexed the real-time mRNA quantification for three T7 promoters on a RT-PCR thermocycler by using BFQ probes with different colored fluorophores that were specific for each target. Ultimately, we created an inexpensive multiplexed method to quantify mRNA production in real-time, and future research could use these methods to measure the affinity of transcriptional repressors to their target DNA sequence.

Transcriptomic and Proteomic Analysis of Gardnerella vaginalis Responding to Acidic pH and Hydrogen Peroxide Stress.

Gardnerella vaginalis is the main pathogen that causes bacterial vaginosis. In the healthy vaginal microecological environment of a woman, the lactobacilli produce lactate and hydrogen peroxide to inhibit the growth of pathogens such as G. vaginalis. The lack of lactobacilli results in a high pH and low hydrogen peroxide in the vagina which facilitate G. vaginalis growth, leading to the imbalance of the vaginal microecology. In this study, lactate and hydrogen peroxide were added to a G. vaginalis culture medium to simulate the co-culture of the lactobacilli and G. vaginalis, and then the genes related to the stress response of G. vaginalis were identified using transcriptomics and proteomics. It was indicated that, among all the upregulated genes, most of them encoded transporters associated with the efflux of harmful substances, and the majority of the downregulated genes were related to the biofilm formation and epithelial cell adhesion. This study may help find new drug targets for G. vaginalis for the development of novel therapies for bacterial vaginosis.

A fast RT-qPCR system significantly shortens the time for SARS-CoV-2 nucleic acid test.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious threat to global development. Rapid and accurate diagnosis is critical for containing the pandemic and treating patients in time. As the gold standard for SARS-CoV-2 diagnosis, the qualitative reverse transcription-PCR (RT-qPCR) test has long been criticized for its long detection time. In this study, we optimized the primers and probes targeting SARS-CoV-2 ORF1ab and N gene designed by the Chinese Center for Disease Control and Preventions (CDC) to increase their Tm values to meet the optimal elongation temperature of Taq DNA polymerase, thus greatly shortened the elongation time. The higher elongation temperature in turn narrowed the temperature range of the reaction and saved more time. In addition, by shortening the distance between the fluorophore at the 5' end and the quencher in the middle we got a probe with higher signal-to-noise ratio. Finally, by using all these measures and optimized RT-qPCR program we successfully reduced the time (nucleic acid extraction step is not included) for nucleic acid test from 74 min to 26 min.

SARS-CoV-2 RdRp uses NDPs as a substrate and is able to incorporate NHC into RNA from diphosphate form molnupiravir.

The coronavirus disease 2019 has been ravaging throughout the world for three years and has severely impaired both human health and the economy. The causative agent, severe acute respiratory syndrome coronavirus 2 employs the viral RNA dependent RNA polymerase (RdRp) complex for genome replication and transcription, making RdRp an appealing target for antiviral drug development. Systematic characterization of RdRp will undoubtedly aid in the development of antiviral drugs targeting RdRp. Here, our research reveals that RdRp can recognize and utilize nucleoside diphosphates as a substrate to synthesize RNA with an efficiency of about two thirds of using nucleoside triphosphates as a substrate. Nucleoside diphosphates incorporation is also template-specific and has high fidelity. Moreover, RdRp can incorporate ß-d-N4-hydroxycytidine into RNA while using diphosphate form molnupiravir as a substrate. This incorporation results in genome mutation and virus death. It is also observed that diphosphate form molnupiravir is a better substrate for RdRp than the triphosphate form molnupiravir, presenting a new strategy for drug design.

Engineering of Burkholderia thailandensis strain E264 serves as a chassis for expression of complex specialized metabolites.

Heterologous expression is an indispensable approach to exploiting natural products from phylogenetically diverse microbial communities. In this study, we constructed a heterologous expression system based on strain Burkholderia thailandensis E264 by deleting efflux pump genes and screening constitutive strong promoters. The biosynthetic gene cluster (BGC) of disorazol from Sorangium cellulosum So ce12 was expressed successfully with this host, and the yield of its product, disorazol F2, rather than A1, was improved to 38.3 mg/L by promoter substitution and insertion. In addition to the disorazol gene cluster, the BGC of rhizoxin from Burkholderia rhizoxinica was also expressed efficiently, whereas no specific peak was detected when shuangdaolide BGC from Streptomyces sp. B59 was transformed into the host. This system provides another option to explore natural products from different phylogenetic taxa.

Antibiotic resistance and pathogenicity assessment of various Gardnerella sp. strains in local China.

Gardnerella overgrowth is the primary cause of bacterial vaginosis (BV), a common vaginal infection with incidences as high as 23-29% worldwide. Here, we studied the pathogenicity, drug resistance, and prevalence of varying Gardnerella spp. We isolated 20 Gardnerella strains from vaginal samples of 31 women in local China. Ten strains were then selected via phylogenetic analysis of cpn60 and vly gene sequences to carry out genome sequencing and comparative genomic analysis. Biofilm-formation, sialidase, and antibiotic resistance activities of the strains were characterized. All strains showed striking heterogeneity in genomic structure, biofilm formation and drug resistance. Two of the ten strains, JNFY3 and JNFY15, were classified as Gardnerella swidsinskii and Gardnerella piotii, respectively, according to their phenotypic characteristics and genome sequences. In particular, seven out of the ten strains exhibited super resistance (≥ 128 µg/mL) to metronidazole, which is the first line of treatment for BV in China. Based on the biochemical and genomic results of the strains, we proposed a treatment protocol of prevalent Gardnerella strains in local China, which provides the basis for accurate diagnosis and therapy.

A virus-target host proteins recognition method based on integrated complexes data and seed extension.

BACKGROUND: Target drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus-target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus-target host proteins and reduce the cost of disease treatment. RESULTS: Since protein complexes contain virus-target host proteins, it is reasonable to predict virus-target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus-target host proteins, the human protein-protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus-target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments. CONCLUSIONS: Our proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus-target host proteins.

Salmonella Facilitates Iron Acquisition through UMPylation of Ferric Uptake Regulator.

Iron limitation is a universal strategy of host immunity during bacterial infection. However, the mechanisms by which pathogens antagonize host nutritional immunity have not been fully elucidated. Here, we identified a requirement for the UMPylator YdiU for this process in Salmonella. The expression of YdiU was dramatically induced by the metal starvation signal. The intracellular iron content was much lower in the ΔydiU strain than in wild-type Salmonella, and the ΔydiU strain exhibited severe growth defect under metal deficiency environments. Genome-wide expression analyses revealed significantly decreased expression of iron uptake genes in ΔydiU strain compared with the wild-type strain. Interestingly, YdiU did not affect the expression level of the major iron uptake regulator Fur but directly UMPylated Fur on its H118 residue in vivo and in vitro. UMPylation destroyed the Fur dimer, promoted Fur aggregation, and eliminated the DNA-binding activity of Fur, thus abolishing the ability of Fur to inhibit iron uptake. Restricting Fur to the deUMPylated state dramatically eliminates Salmonella iron uptake in iron deficiency environments. In parallel, YdiU facilitates Salmonella survival within host cells by regulating the iron uptake pathway. IMPORTANCE Salmonella is the major pathogen causing bacterial enteric illness in both humans and animals. Iron availability is strictly controlled upon Salmonella entry into host cells. The mechanisms by which Salmonella balances the acquisition of sufficient iron while preventing a toxic overload has not been fully understood. Here, we reveal a novel regulation process of iron acquisition mediated by the UMPylator YdiU. Fur acts as the central regulator of bacterial iron homeostasis. YdiU UMPylates Fur on H118 and prevents Fur from binding to target DNA, thus activating the expression of iron uptake genes under iron-deficient conditions. We describe the first posttranslational modification-based regulation of Fur and highlight a potential mechanism by which Salmonella can adapt to eliminate host nutritional immunity.

Deep neural learning based protein function prediction.

It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explore the relationships between single protein feature and function tags. The practical methods for large-scale multi-features protein prediction still need to be studied in depth. This paper proposes a DNN based protein function prediction approach IGP-DNN. This method uses Grasshopper Optimization Algorithm (GOA) and Intuitionistic Fuzzy c-Means clustering (IFCM) based protein function modules extracting algorithm to extract the features of protein modules, utilizing Kernel Principal Component Analysis (KPCA) method to reduce the dimensionality of the protein attribute information, and integrating module features and attribute features. Inputting integrated data into DNN through multiple hidden layers to classify proteins and predict protein functions. In the experiments, the F-measure value of IGP-DNN on the DIP dataset reaches 0.4436, which shows better performance.

Structure-Based Primer Design Minimizes the Risk of PCR Failure Caused by SARS-CoV-2 Mutations.

The coronavirus disease 2019 (COVID-19) has caused and is still causing tremendous damage to the global economy and human health. Qualitative reverse transcription-PCR (RT-qPCR) is the golden standard for COVID-19 test. However, the SARS-CoV-2 variants may not only make vaccine less effective but also evade RT-qPCR test. Here we suggest an innovative primer design strategy for the RT-qPCR test of SARS-CoV-2. The principle is that the primers should be designed based on both the nucleic acid sequence and the structure of the protein encoded. The three nucleotides closest to the 3' end of the primer should be the codon which encodes the tryptophan in the structure core. Based on this principle, we designed a pair of primers targeting the nucleocapsid (N) gene. Since tryptophan is encoded by only one codon, any mutation that occurs at this position would change the amino acid residue, resulting in an unstable N protein. This means that this kind of SARS-CoV-2 variant could not survive. In addition, both our data and previous reports all indicate that the mutations occurring at other places in the primers do not significantly affect the RT-qPCR result. Consequently, no SARS-CoV-2 variant can escape detection by the RT-qPCR kit containing the primers designed based on our strategy.

DexA70, the Truncated Form of a Self-Produced Dextranase, Effectively Disrupts Streptococcus mutans Biofilm.

Billions of people suffer from dental caries every year in spite of the effort to reduce the prevalence over the past few decades. Streptococcus mutans is the leading member of a specific group of cariogenic bacteria that cause dental caries. S. mutans forms biofilm, which is highly resistant to harsh environment, host immunity, and antimicrobial treatments. In this study, we found that S. mutans biofilm is highly resistant to both antimicrobial agents and lysozyme. DexA70, the truncated form of DexA (amino acids 100-732), a dextranase in S. mutans, prevents S. mutans biofilm formation and disassembles existing biofilms within minutes at nanomolar concentrations when supplied exogenously. DexA70 treatment markedly enhances biofilm sensitivity to antimicrobial agents and lysozyme, indicating its great potential in combating biofilm-related dental caries.

Protein functional module identification method combining topological features and gene expression data.

BACKGROUND: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. RESULTS: This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. CONCLUSIONS: The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.

Multi-objective grasshopper optimization algorithm based on multi-group and co-evolution.

The balance between exploration and exploitation is critical to the performance of a Meta-heuristic optimization method. At different stages, a proper tradeoff between exploration and exploitation can drive the search process towards better performance. This paper develops a multi-objective grasshopper optimization algorithm (MOGOA) with a new proposed framework called the Multi-group and Co-evolution Framework which can archive a fine balance between exploration and exploitation. For the purpose, a grouping mechanism and a co-evolution mechanism are designed and integrated into the framework for ameliorating the convergence and the diversity of multi-objective optimization solutions and keeping the exploration and exploitation of swarm intelligence algorithm in balance. The grouping mechanism is employed to improve the diversity of search agents for increasing coverage of search space. The co-evolution mechanism is used to improve the convergence to the true Pareto optimal front by the interaction of search agents. Quantitative and qualitative outcomes prove that the framework prominently ameliorate the convergence accuracy and convergence speed of MOGOA. The performance of the presented algorithm has been benchmarked by several standard test functions, such as CEC2009, ZDT and DTLZ. The diversity and convergence of the obtained multi-objective optimization solutions are quantitatively and qualitatively compared with the original MOGOA by using two performance indicators (GD and IGD). The results on test suits show that the diversity and convergence of the obtained solutions are significantly improved. On several test functions, some statistical indicators are more than doubled. The validity of the results has been verified by the Wilcoxon rank-sum test.

Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning.

Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using Support Vector Machine, Random Forest (RF), AdaBoost (adaptive boosting), GBDT (gradient boosting decision tree), and XGBoost (extreme gradient boosting). After comparing the performance of these ensemble models, SMOTE algorithms with XGBoost achieved an F1 score of 0.94 with the balanced A. thaliana gene datasets, but a lower score with the unbalanced datasets. The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately. The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost models. Thus, SMOTE-ENN-XGBoost provides a theoretical basis for developing evaluation criteria for identifying orphan genes in unbalanced and biological datasets.

The YdiU Domain Modulates Bacterial Stress Signaling through Mn2+-Dependent UMPylation.

Sensing stressful conditions and adjusting the cellular metabolism to adapt to the environment are essential activities for bacteria to survive in variable situations. Here, we describe a stress-related protein, YdiU, and characterize YdiU as an enzyme that catalyzes the covalent attachment of uridine-5'-monophosphate to a protein tyrosine/histidine residue, an unusual modification defined as UMPylation. Mn2+ serves as an essential co-factor for YdiU-mediated UMPylation. UTP and Mn2+ binding converts YdiU to an aggregate-prone state facilitating the recruitment of chaperones. The UMPylation of chaperones prevents them from binding co-factors or clients, thereby impairing their function. Consistent with the recent finding that YdiU acts as an AMPylator, we further demonstrate that the self-AMPylation of YdiU padlocks its chaperone-UMPylation activity. A detailed mechanism is proposed based on the crystal structures of Apo-YdiU and YdiU-AMPNPP-Mn2+ and on molecular dynamics simulation models of YdiU-UTP-Mn2+ and YdiU-UTP-peptide. In vivo data demonstrate that YdiU effectively protects Salmonella from stress-induced ATP depletion through UMPylation.

YdiV regulates Escherichia coli ferric uptake by manipulating the DNA-binding ability of Fur in a SlyD-dependent manner.

Iron is essential for all bacteria. In most bacteria, intracellular iron homeostasis is tightly regulated by the ferric uptake regulator Fur. However, how Fur activates the iron-uptake system during iron deficiency is not fully elucidated. In this study, we found that YdiV, the flagella gene inhibitor, is involved in iron homeostasis in Escherichia coli. Iron deficiency triggers overexpression of YdiV. High levels of YdiV then transforms Fur into a novel form which does not bind DNA in a peptidyl-prolyl cis-trans isomerase SlyD dependent manner. Thus, the cooperation of YdiV, SlyD and Fur activates the gene expression of iron-uptake systems under conditions of iron deficiency. Bacterial invasion assays also demonstrated that both ydiV and slyD are necessary for the survival and growth of uropathogenic E. coli in bladder epithelial cells. This reveals a mechanism where YdiV not only represses flagella expression to make E. coli invisible to the host immune system, but it also promotes iron acquisition to help E. coli overcome host nutritional immunity.